Recently, \(k\) -nearest neighbors (\(K-\) NNs) is applied on the PLM representations for language modeling [1]} and dialogue [2]}.
We extend this line of study and apply \(k-\) NN for
cross-domain generalization in DQD, where the models are trained on data from a source domain, and applied on data from a target domain.
To do so, we represent pairs from source and target in a common representation space and then score target pairs using nearest neighbors
in the source pairs. Figure REF  shows an illustration of this procedure.
